Prognostics of lithium-ion batteries based on flexible support vector regression

Accurate estimation of the remaining useful life of lithium-ion batteries plays an important role in the prognostic and health management (PHM). The traditional empirical data-driven approaches for RUL prediction usually need multidimensional input physical characteristics including the current, voltage, usage duration, battery temperature, and ambient temperature. From the capacity fading analysis of lithium-ion batteries, this paper found the energy efficiency and battery working temperature closely related to capacity degradation, which not only consider all performance metrics of lithium-ion batteries with regard to the RUL but also take the relationships between some performance metrics into account. Thus, we devise a non-iterative prediction model based on flexible support vector regression (F-SVR) taking the energy efficiency and battery working temperature as input physical characteristics. The F-SVR method divides the training sample dataset into several regions according to the distribution complexity and then generates different parameters set for each region, so it can accurately fit the RUL trend. The proposed prognostic method has high prediction accuracy and the proposed model needs fewer dimensions input data than the traditional empirical models from the experimental results.